This is a blog by a former CEO of a large Boston hospital to share thoughts about negotiation theory and practice, leadership training and mentoring, and teaching.

Sunday, February 07, 2016

There is no Holy Grail, just small chalices

Given the stakes to society and the persistent growth in
health care delivery costs throughout the developed nations, there is an
understandable desire to achieve the “breakthrough” technological solutions
that will result in a substantial disruption in diagnostic and treatment
practices and patterns that have evolved over the decades.Well intentioned and intelligent people with
thoughtful ideas are focused on ways to achieve these solutions.Investors, seeing the large (and growing)
percentage of each nation’s GDP that is devoted to health care, likewise hunger
for the opportunity to grab even a small portion of that wealth.

As I noted in a blog post last year, an area that
consumes tremendous energy is the search for the Holy Grail of decision support
products that would mine health care “big data.” People are looking for the
algorithms that could help doctors—in real time—analyze the condition of
patients and put in place more efficient and efficacious diagnostic regimes and
treatment modalities. I explained in
that blog post why these efforts will fail. Let me summarize:

1 -- The data that is collected is not reliable enough to
draw connections between patient characteristics, clinical decisions and
outcomes.It is not reliable for two
reasons.First, it is simply not
reliable.Much data that is collected
and/or coded in hospitals and physician practices is done so poorly, or in a
format that is not clinically accurate.Second, it is likely to be characterized by such wide standard
deviations as to make it unsuitable for predictive purposes.

2 -- It is unlikely that the algorithms that are designed to
produce work rules will be trusted by doctors. In part, this is due to the standard deviation problem noted above. That is, the models will not be sufficiently
rigorous in their predictive capacity. Maybe more important, there is a general lack of trust on the part of doctors
with regard to using formulaic approaches in their practices. While doctors are the victims of many kinds of
cognitive errors—diagnostic anchoring, confirmation bias, and the like--they are often not trained to reflect on and catch
these biases. They are trained instead
to trust their own judgment and take personal responsibility for their
patients. It would be but a small
minority of doctors who would be able to overcome those biases and that
training to use big-data-driven decision support tools—even if such tools were
able to overcome the statistical difficulties mentioned above.

3 – The process for selling such systems into the hospital
market is complex and almost infinitely slow. The sales cycle will kill off all but the most highly capitalized
firms. Even excellent products will
often wither and die on the vine.

Does this suggest that there is no potential for disruptive technologies
that can improve health care delivery at a reduced cost? No, but it suggests that there is not a Holy
Grail, but rather a group of smaller, potentially jewel-encrusted,
chalices. Targeted innovation is the way
to go. Think small, think focused, and
think about how to achieve quick results that benefit the doctor, the patient,
and the hospital.

Wait, did I just put the doctor first on that list? The Ptolemeic health care system has the doctor at the center of the
solar system, and it will be that way for a long time to come. Unless your product helps the doctor feel
that they are doing a better job and can fit into their work flow, it’s not
worth pursuing.

I’ll provide an example that originated in Melbourne,
produced by a firm called Global Kinetics. The
approach is described in this article. A
Parkinson’s patient wears a simple device on their wrist for a week or
two. The accelerometer contained in the
device correlates the extent of the patient’s movement disorder with the drug
dosages they have taken. (The “watch”
also, by the way, provides the patient with a reminder to take the drug at the
specified times, leading to a higher level of adherence and providing a higher level of precision to the experiment.) The
report is transmitted to a standard hand-held device, using a patient code that
is fully privacy protected.

The technology and the reports produced by this approach do
not substitute for the judgment of the neurologist. Rather that judgment—previously based on
trial and error--is enhanced by a real-time, patient specific experiment. The process can be repeated as often as the
doctor deems necessary--more often for a patient suffering a rapid
deterioration from the disease, and less often for a more stable patient.

The device is not bought by the hospital, and so it bypasses
the highly competitive capital budgeting process. Rather, the product is provided as part of a
service offering, the test result that is provided to the doctor. The fee for each report is well within the
normal operating budget of the neurology department, requiring no special
allocation of funds. In short,
acceptance simply requires a decision by the doctors themselves.

I offer this as a perfect example of a jeweled chalice. Simple hardware and software technologies;
easily incorporated into the doctors' workflow; enhancing their ability to
exercise professional judgment; and offered in a sales process that does not
create competition among hospital factions and is consistent with normal budget
processes. It is by this path that
technology can disrupt health care—one carefully designed step at a time.

2 comments:

Paul, briefly as per your request :1. Is true for professionals data, but don't forget the stream of patient generated data that is coming up.2. Once it has been proven that (some of those) algorithm DO work and are even better than them, they WILL have to trust them. 3. Agreed, although there is a whole new channel coming up via crowdfunding. (i.e. apps)

In general : the patient will own more of his own data (producing it himself) granting access to the professional by a subscription. Big secure platforms (like Health Suite Digital Platform from Philips) will overtake much of the now incompetent data gathering, and make sense of that OR showing the faulty data.

Re Lucien's comment #2: I'm not sure that it's true to say that doctors will realize proven algorithms work better and will thus trust them. After all, there's very strong evidence for such things as central line protocols and use of VAP bundles, yet broad adoption of these kinds of evidence-based practices is far from widespread. Why would adoption of algorithms be different?